Prediction of linear B-cell epitopes based on protein sequence features and BERT embeddings

Linear B-cell epitopes (BCEs) play a key role in the development of peptide vaccines and immunodiagnostic reagents. Therefore, the accurate identification of linear BCEs is of great importance in the prevention of infectious diseases and the diagnosis of related diseases. The experimental methods used to identify BCEs are both expensive and time-consuming and they do not meet the demand for identification of large-scale protein sequence data. As a result, there is a need to develop an efficient and accurate computational method to rapidly identify linear BCE sequences. In this work, we developed the new linear BCE prediction method LBCE-BERT. This method is based on peptide chain sequence information and natural language model BERT embedding information, using an XGBoost classifier. The models were trained on three benchmark datasets. The model was training on three benchmark datasets for hyperparameter selection and was subsequently evaluated on several test datasets. The result indicate that our proposed method outperforms others in terms of AUROC and accuracy. The LBCE-BERT model is publicly available at: https://github.com/Lfang111/LBCE-BERT .

Advances in antibody discovery from human BCR repertoires

Antibodies make up an important and growing class of compounds used for the diagnosis or treatment of disease. While traditional antibody discovery utilized immunization of animals to generate lead compounds, technological innovations have made it possible to search for antibodies targeting a given antigen within the repertoires of B cells in humans. Here we group these innovations into four broad categories: cell sorting allows the collection of cells enriched in specificity to one or more antigens; BCR sequencing can be performed on bulk mRNA, genomic DNA or on paired (heavy-light) mRNA; BCR repertoire analysis generally involves clustering BCRs into specificity groups or more in-depth modeling of antibody-antigen interactions, such as antibody-specific epitope predictions; validation of antibody-antigen interactions requires expression of antibodies, followed by antigen binding assays or epitope mapping. Together with innovations in Deep learning these technologies will contribute to the future discovery of diagnostic and therapeutic antibodies directly from humans.

Relationship between B-cell epitope structural properties and the immunogenicity of blood group antigens: Outlier properties of the Kell K1 antigen

BACKGROUND

The immunogenicities of polypeptide blood group antigens vary, despite most being created by single amino acid (AA) substitutions. To study the basis of these differences, we employed an immunoinformatics approach to determine whether AA substitution sites of blood group antigens have structural features typical of B-cell epitopes and whether the extent of B-cell epitope properties is positively related to immunogenicity.

STUDY DESIGN AND METHODS

Fifteen structural property prediction programs were used to determine the likelihood of β-turns, surface accessibility, flexibility, hydrophilicity, particular AA composition and AA pairs, and other B-cell epitope properties at AA substitution sites of polypeptide blood group antigens.

RESULTS

AA substitution sites of Lu^a , Jk^a , E, c, M, Fy^a , C, and S were each located in regions with at least two structural features typical of B-cell epitopes. The substitution site of K, the most immunogenic non-ABO/D antigen, scored the lowest for most B-cell epitope properties and was the only one not predicted to be part of a linear B-cell epitope. The most immunogenic antigens studied (K, Jk^a , Lu^a , E) had B-cell epitope structural properties determined by the fewest programs; the least immunogenic antigens (e.g., Fy^a , S, C, c) had B-cell epitope properties according to the most programs.

DISCUSSION

Counter to prediction, the immunogenicity of polypeptide blood group antigens was not positively related to B-cell epitope structural features present at their AA-substitution sites. Instead, it tended to be negatively related. The AA-substitution site of the most immunogenic non-ABO/D antigen, K, had the least B-cell epitope features.

Production of the sheep pox virus structural protein SPPV117 in tobacco chloroplasts

OBJECTIVE

A chloroplast transgenic approach was assessed in order to produce a structural protein SPPV117 of sheep pox virus in Nicotiana tabacum for the future development of a plant-based subunit vaccine against sheep pox.

RESULTS

Two DNA constructs containing SPPV117 coding sequence under the control of chloroplast promoter and terminator of psbA gene or rrn promoter and rbcL terminator were designed and inserted into the chloroplast genome by a biolistic method. The transgenic plants were selected via PCR analysis. Northern and Western blot analysis showed expression of the transgene at transcriptional and translational levels, respectively. The recombinant protein accumulated to about 0.3% and 0.9% of total soluble protein in leaves when expressed from psbA and rrn promoter, respectively. Plant-produced SPPV117 protein was purified using metal affinity chromatography and the protein yield was 19.67  ±  1.25 µg g-1 (FW). The serum of a sheep infected with the virus recognised the chloroplast-produced protein indicating that the protein retains its antigenic properties.

CONCLUSIONS

These results demonstrate that chloroplasts are a suitable system for the production of a candidate subunit vaccine against sheep pox.

SeRenDIP-CE: Sequence-based Interface Prediction for Conformational Epitopes

Motivation

Antibodies play an important role in clinical research and biotechnology, with their specificity determined by the interaction with the antigen’s epitope region, as a special type of protein–protein interaction (PPI) interface. The ubiquitous availability of sequence data, allows us to predict epitopes from sequence in order to focus time-consuming wet-lab experiments toward the most promising epitope regions. Here, we extend our previously developed sequence-based predictors for homodimer and heterodimer PPI interfaces to predict epitope residues that have the potential to bind an antibody.

Results

We collected and curated a high quality epitope dataset from the SAbDab database. Our generic PPI heterodimer predictor obtained an AUC-ROC of 0.666 when evaluated on the epitope test set. We then trained a random forest model specifically on the epitope dataset, reaching AUC 0.694. Further training on the combined heterodimer and epitope datasets, improves our final predictor to AUC 0.703 on the epitope test set. This is better than the best state-of-the-art sequence-based epitope predictor BepiPred-2.0. On one solved antibody–antigen structure of the COVID19 virus spike receptor binding domain, our predictor reaches AUC 0.778. We added the SeRenDIP-CE Conformational Epitope predictors to our webserver, which is simple to use and only requires a single antigen sequence as input, which will help make the method immediately applicable in a wide range of biomedical and biomolecular research.

Availability and implementation

Webserver, source code and datasets at www.ibi.vu.nl/programs/serendipwww/.

Supplementary information

Supplementary data are available at Bioinformatics online.

Linear B-Cell Epitope Prediction for In Silico Vaccine Design: A Performance Review of Methods Available via Command-Line Interface

Linear B-cell epitope prediction research has received a steadily growing interest ever since the first method was developed in 1981. B-cell epitope identification with the help of an accurate prediction method can lead to an overall faster and cheaper vaccine design process, a crucial necessity in the COVID-19 era. Consequently, several B-cell epitope prediction methods have been developed over the past few decades, but without significant success. In this study, we review the current performance and methodology of some of the most widely used linear B-cell epitope predictors which are available via a command-line interface, namely, BcePred, BepiPred, ABCpred, COBEpro, SVMTriP, LBtope, and LBEEP. Additionally, we attempted to remedy performance issues of the individual methods by developing a consensus classifier, which combines the separate predictions of these methods into a single output, accelerating the epitope-based vaccine design. While the method comparison was performed with some necessary caveats and individual methods might perform much better for specialized datasets, we hope that this update in performance can aid researchers towards the choice of a predictor, for the development of biomedical applications such as designed vaccines, diagnostic kits, immunotherapeutics, immunodiagnostic tests, antibody production, and disease diagnosis and therapy.

Immunization with SP_1992 (DiiA) Protein of Streptococcus pneumoniae Reduces Nasopharyngeal Colonization and Protects against Invasive Disease in Mice

Knowledge-based vaccinology can reveal uncharacterized antigen candidates for a new generation of protein-based anti-pneumococcal vaccines. DiiA, encoded by the sp_1992 locus, is a surface protein containing either one or two repeats of a 37mer N-terminal motif that exhibits low interstrain variability. DiiA belongs to the core proteome, contains several conserved B-cell epitopes, and is associated with colonization and pathogenesis. Immunization with DiiA protein via the intraperitoneal route induced a strong IgG response, including different IgG subtypes. Vaccination with DiiA increased bacterial clearance and induced protection against sepsis, conferring 70% increased survival at 48 h post-infection when compared to the adjuvant control. The immunogenic response and survival rates in mice immunized with a truncated DiiA version lacking 119 N-terminal residues were remarkably lower, confirming the relevance of the repeat zone in the immunoprotection by DiiA. Intranasal immunization of mice with the entire recombinant protein elicited mucosal IgG and IgA responses that reduced bacterial colonization of the nasopharynx, confirming that this protein might be a vaccine candidate for reducing the carrier rate. DiiA constitutes an example of how functionally unannotated proteins may still represent promising candidates that can be used in prophylactic strategies against the pneumococcal carrier state and invasive disease.

An integrated in silico immuno-genetic analytical platform provides insights into COVID-19 serological and vaccine targets

During COVID-19, diagnostic serological tools and vaccines have been developed. To inform control activities in a post-vaccine surveillance setting, we have developed an online “immuno-analytics” resource that combines epitope, sequence, protein and SARS-CoV-2 mutation analysis. SARS-CoV-2 spike and nucleocapsid proteins are both vaccine and serological diagnostic targets. Using the tool, the nucleocapsid protein appears to be a sub-optimal target for use in serological platforms. Spike D614G (and nsp12 L314P) mutations were most frequent (> 86%), whilst spike A222V/L18F have recently increased. Also, Orf3a proteins may be a suitable target for serology. The tool can accessed from: http://genomics.lshtm.ac.uk/immuno (online); https://github.com/dan-ward-bio/COVID-immunoanalytics (source code).

Deep learning methods improve linear B-cell epitope prediction

Background

B-cell epitopes play important roles in vaccine design, clinical diagnosis, and antibody production. Although some models have been developed to predict linear or conformational B-cell epitopes, their performance is still unsatisfactory. Hundreds of thousands of linear B-cell epitope data have accumulated in the Immune Epitope Database (IEDB). These data can be explored using the deep learning methods, in order to create better predictive models for linear B-cell epitopes.

Results

After data cleaning, we obtained 240,563 peptide samples with experimental evidence from the IEDB database, including 25,884 linear B-cell epitopes and 214,679 non-epitopes. Based on the peptide center, we adapted each peptide to the same length by trimming or extending. A random portion of the data, with the same amount of epitopes and non-epitopes, were set aside as test dataset. Then a same number of epitopes and non-epitopes were randomly selected from the remaining data to build a classifier with the feedforward deep neural network. We built eleven classifiers to form an ensemble prediction model. The model will report a peptide as an epitope if it was classified as epitope by all eleven classifiers. Then we used the test data set to evaluate the performance of the model using the area value under the receiver operating characteristic (ROC) curve (AUC) as an indicator. We established 40 models to predict linear B-cell epitopes of length from 11 to 50 separately, and found that the AUC value increased with the length and tended to be stable when the length was 38. Repeated results showed that the models constructed by this method were robust. Tested on our and two public test datasets, our models outperformed current major models available.

Conclusions

We applied the feedforward deep neural network to the large amount of linear B-cell epitope data with experimental evidence in the IEDB database, and constructed ensemble prediction models with better performance than the current major models available. We named the models as DLBEpitope and provided web services using the models at http://ccb1.bmi.ac.cn:81/dlbepitope/.

Optimisation of human VH domain antibodies specific to Mycobacterium tuberculosis heat shock protein (HSP16.3)

Mycobacterium tuberculosis (Mtb) 16.3 kDa heat shock protein 16.3 (HSP16.3) is a latency-associated antigen that can be targeted for latent tuberculosis (TB) diagnostic and therapeutic development. We have previously developed human V_H domain antibodies (dAbs; clone E3 and F1) specific against HSP16.3. In this work, we applied computational methods to optimise and design the antibodies in order to improve the binding affinity with HSP16.3. The V_H domain antibodies were first docked to the dimer form of HSP16.3 and further sampled using molecular dynamics simulation. The calculated binding free energy of the HSP16.3-dAb complexes showed non-polar interactions were responsible for the antigen-antibody association. Per-residue free energy decomposition and computational alanine scanning have identified one hotspot residue for E3 (Y391) and 4 hotspot residues for F1 (M394, Y396, R397 and M398). These hotspot residues were then mutated and evaluated by binding free energy calculations. Phage ELISA assay was carried out on the potential mutants (E3_Y391W, F1_M394E, F1_R397N and F1_M398Y). The experimental assay showed improved binding affinities of E3_Y391W and F1_M394E against HSP16.3 compared with the wild type E3 and F1. This case study has thus showed in silico methods are able to assist in optimisation or improvement of antibody-antigen binding.

Postgenomic Approaches and Bioinformatics Tools to Advance the Development of Vaccines against Bacteria of the Burkholderia cepacia Complex

Bacteria of the Burkholderia cepacia complex (Bcc) remain an important cause of morbidity and mortality among patients suffering from cystic fibrosis. Eradication of these pathogens by antimicrobial therapy often fails, highlighting the need to develop novel strategies to eradicate infections. Vaccines are attractive since they can confer protection to particularly vulnerable patients, as is the case of cystic fibrosis patients. Several studies have identified specific virulence factors and proteins as potential subunit vaccine candidates. So far, no vaccine is available to protect from Bcc infections. In the present work, we review the most promising postgenomic approaches and selected web tools available to speed up the identification of immunogenic proteins with the potential of conferring protection against Bcc infections.

Classification epitopes in groups based on their protein family

Background

The humoral immune system response is based on the interaction between antibodies and antigens for the clearance of pathogens and foreign molecules. The interaction between these proteins occurs at specific positions known as antigenic determinants or B-cell epitopes. The experimental identification of epitopes is costly and time consuming. Therefore the use of in silico methods, to help discover new epitopes, is an appealing alternative due the importance of biomedical applications such as vaccine design, disease diagnostic, anti-venoms and immune-therapeutics. However, the performance of predictions is not optimal been around 70% of accuracy. Further research could increase our understanding of the biochemical and structural properties that characterize a B-cell epitope.

Results

We investigated the possibility of linear epitopes from the same protein family to share common properties. This hypothesis led us to analyze physico-chemical (PCP) and predicted secondary structure (PSS) features of a curated dataset of epitope sequences available in the literature belonging to two different groups of antigens (metalloproteinases and neurotoxins). We discovered statistically significant parameters with data mining techniques which allow us to distinguish neurotoxin from metalloproteinase and these two from random sequences. After a five cross fold validation we found that PCP based models obtained area under the curve values (AUC) and accuracy above 0.9 for regression, decision tree and support vector machine.

Conclusions

We demonstrated that antigen's family can be inferred from properties within a single group of linear epitopes (metalloproteinases or neurotoxins). Also we discovered the characteristics that represent these two epitope groups including their similarities and differences with random peptides and their respective amino acid sequence. These findings open new perspectives to improve epitope prediction by considering the specific antigen's protein family. We expect that these findings will help to improve current computational mapping methods based on physico-chemical due it's potential application during epitope discovery.

Functional Exposed Amino Acids of BauA as Potential Immunogen Against Acinetobacter baumannii

Multidrug-resistant Acinetobacter baumannii is recognized to be among the most difficult antimicrobial-resistant gram negative bacilli to control and treat. One of the major challenges that the pathogenic bacteria face in their host is the scarcity of freely available iron. To survive under such conditions, bacteria express new proteins on their outer membrane and also secrete iron chelators called siderophores. Antibodies directed against these proteins associated with iron uptake exert a bacteriostatic or bactericidal effect against A. baumanii in vitro, by blocking siderophore mediated iron uptake pathways. Attempts should be made to discover peptides that could mimic protein epitopes and possess the same immunogenicity as the whole protein. Subsequently, theoretical methods for epitope prediction have been developed leading to synthesis of such peptides that are important for development of immunodiagnostic tests and vaccines. The present study was designed to in silico resolving the major obstacles in the control or in prevention of the diseases caused by A. baumannii. We exploited bioinformatic tools to better understand and characterize the Baumannii acinetobactin utilization structure of A. baumannii and select appropriate regions as effective B cell epitopes. In conclusion, amino acids 26-191 of cork domain and 321-635 of part of the barrel domain including L4-L9, were selected as vaccine candidates. These two regions contain functional exposed amino acids with higher score of B cell epitopes properties. Majority of amino acids are hydrophilic, flexible, accessible, and favorable for B cells from secondary structure point of view.

Computational peptide vaccinology

Immunoinformatics focuses on modeling immune responses for better understanding of the immune system and in many cases for proposing agents able to modify the immune system. The most classical of these agents are vaccines derived from living organisms such as smallpox or polio. More modern vaccines comprise recombinant proteins, protein domains, and in some cases peptides. Generating a vaccine from peptides however requires technologies and concepts very different from classical vaccinology. Immunoinformatics therefore provides the computational tools to propose peptides suitable for formulation into vaccines. This chapter introduces the essential biological concepts affecting design and efficacy of peptide vaccines and discusses current methods and workflows applied to design successful peptide vaccines using computers.

Theoretical investigation on structural, functional and epitope of a 12 kDa excretory-secretory protein from Toxoplasma gondii

BACKGROUND

Toxoplasma gondii is an intracellular coccidian parasite that causes toxoplasmosis. It was estimated that more than one third of the world population is infected by T. gondii, and the disease is critical in fetuses and immunosuppressed patients. Thus, early detection is crucial for disease diagnosis and therapy. However, the current available toxoplasmosis diagnostic tests vary in their accuracy and the better ones are costly.

RESULTS

An earlier published work discovered a highly antigenic 12 kDa excretory-secretory (ES) protein of T. gondii which may potentially be used for the development of an antigen detection test for toxoplasmosis. However, the three-dimensional structure of the protein is unknown. Since epitope identification is important prior to designing of a specific antibody for an antigen-detection based diagnostic test, the structural elucidation of this protein is essential. In this study, we constructed a three dimensional model of the 12 kDa ES protein. The built structure possesses a thioredoxin backbone which consists of four α-helices flanking five β-strands at the center. Three potential epitopes (6-8 residues) which can be combined into one "single" epitope have been identified from the built structure as the most potential antibody binding site.

CONCLUSION

Together with specific antibody design, this work could contribute towards future development of an antigen detection test for toxoplasmosis.

Identification of amino acid propensities that are strong determinants of linear B-cell epitope using neural networks

BACKGROUND

Identification of amino acid propensities that are strong determinants of linear B-cell epitope is very important to enrich our knowledge about epitopes. This can also help to obtain better epitope prediction. Typical linear B-cell epitope prediction methods combine various propensities in different ways to improve prediction accuracies. However, fewer but better features may yield better prediction. Moreover, for a propensity, when the sequence length is k, there will be k values, which should be treated as a single unit for feature selection and hence usual feature selection method will not work. Here we use a novel Group Feature Selecting Multilayered Perceptron, GFSMLP, which treats a group of related information as a single entity and selects useful propensities related to linear B-cell epitopes, and uses them to predict epitopes.

METHODOLOGY/ PRINCIPAL FINDINGS

We use eight widely known propensities and four data sets. We use GFSMLP to rank propensities by the frequency with which they are selected. We find that Chou's beta-turn and Ponnuswamy's polarity are better features for prediction of linear B-cell epitope. We examine the individual and combined discriminating power of the selected propensities and analyze the correlation between paired propensities. Our results show that the selected propensities are indeed good features, which also cooperate with other propensities to enhance the discriminating power for predicting epitopes. We find that individually polarity is not the best predictor, but it collaborates with others to yield good prediction. Usual feature selection methods cannot provide such information.

CONCLUSIONS/ SIGNIFICANCE

Our results confirm the effectiveness of active (group) feature selection by GFSMLP over the traditional passive approaches of evaluating various combinations of propensities. The GFSMLP-based feature selection can be extended to more than 500 remaining propensities to enhance our biological knowledge about epitopes and to obtain better prediction. A graphical-user-interface version of GFSMLP is available at: http://bio.classcloud.org/GFSMLP/.

Prediction of epitopes in closely related proteins using a new algorithm

Latrophilin 1 (presynaptic receptor) binds α-latrotoxin from black widow spider venom and regulates neurotransmitter release from nerve endings. The study of the mechanism of action of this receptor is impeded by the existence of closely related latrophilins 2 and 3. A profile of differences detecting the most differing and identical sites in several proteins was developed in order to obtain highly specific antibodies for differentiation between isoforms of related proteins. In addition, we used an algorithm for prediction of immunogenic sites of the protein, based on the basic vector method. The peptides selected using this algorithm were used for immunization of animals. The resultant sera exhibited the estimated specificity and high affinity for the corresponding receptor forms.